Staff Data Engineer

At Wealthfront we have an ambitious vision to optimize and automate all your personal finances. By delivering our service exclusively through software, we can also offer very low fees and account minimums. Over the past six years our clients have rewarded us with $10 billion to manage and we have attracted some of the best venture capital firms in the business including Benchmark Capital, Greylock, Index Ventures and Social Capital. We recently closed a $75 million round of funding from Tiger Global and are rapidly growing our team. So if you're passionate about helping people secure their ambitions while helping to change an industry, keep reading.

We have a goal: the more clients we have and the more information each of our clients shares with us, the better experience we can provide to all of our clients. To do this, we need to both scale our platform to handle our growing client base and deliver new features that take advantage of the increasing amount of information we have. Our data engineering team is at the center of this.

We’re looking for engineers excited to help scale our existing data infrastructure and build out new compute capabilities. This includes making tradeoffs between online, offline, and streaming architectures, as well as learning the product well enough to understand the impact these decisions will make on clients.

What You'll Work On

Data pipelining infrastructure: Our data moves around a lot. We need it available for batch and real-time compute, and we need to expose it for use by both our backend platform and data analytics. Message brokers, key-value stores, and job schedulers are some tools our team works with. You’ll help identify improvements for our architecture, designing and implementing solutions to get data around more efficiently.

Spark: As data engineers, we want to move fast, and we want our code to move fast as well. We have a batch compute platform using Spark, and we’ve recently built out a near-realtime platform using Spark Streaming. We are continuing to increase the performance of our data pipelines while simultaneously increasing the complexity of the jobs that run on top of them. You'll be expected to improve the infrastructure while you implement on top of it.

Data quality: A model is only good if it is correct and built on recent data. We put a strong emphasis on data quality. We write unit tests to verify the functional correctness of each module and meta-tests to guard against common programming errors. Throughout our data pipelines we run automated sanity checks on live data, alerting if any data is stale or values fall outside of expected ranges.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.